Learning Optimal Reserve Price against Non-myopic Bidders

TL;DR

This paper develops algorithms for setting optimal reserve prices in repeated auctions with strategic, non-myopic bidders, achieving low regret under certain market and bidder impatience conditions.

Contribution

It introduces novel algorithms that attain small regret in complex auction settings with strategic bidders, leveraging differential privacy techniques.

Findings

Algorithms achieve low regret in large markets or with impatient bidders.

The approach controls information revelation to bidders effectively.

Techniques combine differential privacy with online learning.

Abstract

We consider the problem of learning optimal reserve price in repeated auctions against non-myopic bidders, who may bid strategically in order to gain in future rounds even if the single-round auctions are truthful. Previous algorithms, e.g., empirical pricing, do not provide non-trivial regret rounds in this setting in general. We introduce algorithms that obtain small regret against non-myopic bidders either when the market is large, i.e., no bidder appears in a constant fraction of the rounds, or when the bidders are impatient, i.e., they discount future utility by some factor mildly bounded away from one. Our approach carefully controls what information is revealed to each bidder, and builds on techniques from differentially private online learning as well as the recent line of works on jointly differentially private algorithms.